Disable negative cache for table metadata

[brfrn169]

Description

This PR disables negative caching for table metadata by migrating from Guava Cache to Caffeine and changing the cache behavior to not cache null values.

Previously, TableMetadataManager and TransactionTableMetadataManager used Guava's LoadingCache which cached Optional<TableMetadata> values. When a table was not found (returning Optional.empty()), this negative result was cached. This caused problems when:

1. A table is queried before it exists
2. The table is subsequently created
3. The cache still returns the cached negative result

Related issues and/or PRs

N/A

Changes made

• Added Caffeine dependency (version 2.9.3) to the project.
• Migrated from com.google.common.cache.LoadingCache to com.github.benmanes.caffeine.cache.LoadingCache.
• Updated TableMetadataManager and TransactionTableMetadataManager to use Caffeine's cache API.

Checklist

The following is a best-effort checklist. If any items in this checklist are not applicable to this PR or are dependent on other, unmerged PRs, please still mark the checkboxes after you have read and understood each item.

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes.
• I have considered whether similar issues could occur in other products, components, or modules if this PR is for bug fixes.
• Any remaining open issues linked to this PR are documented and up-to-date (Jira, GitHub, etc.).
• Tests (unit, integration, etc.) have been added for the changes.
• My changes generate no new warnings.
• Any dependent changes in other PRs have been merged and published.

Additional notes (optional)

N/A

Release notes

N/A

[gemini-code-assist]

Summary of Changes

Hello @brfrn169, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical caching bug where the system would incorrectly cache the absence of table metadata. This led to scenarios where newly created tables were not discoverable until the cache expired. The solution involves migrating the underlying caching library from Guava to Caffeine, which, by design, does not store null values, thereby eliminating the negative caching behavior and improving the reliability of table metadata lookups.

Highlights

• Negative Cache Fix: The pull request resolves an issue where non-existent table metadata was being negatively cached, preventing access to tables that were created after an initial failed lookup.
• Caching Library Migration: The caching mechanism has been migrated from Guava's LoadingCache to Caffeine's LoadingCache. Caffeine inherently does not cache null values, which directly addresses the negative caching problem.
• Dependency Update: The Caffeine library (version 2.9.3) has been added as a new dependency to the project.
• Code Refactoring: The TableMetadataManager and TransactionTableMetadataManager classes have been updated to use Caffeine's API, including changes to cache type parameters (removing Optional) and exception handling.
• Enhanced Test Coverage: New unit tests have been added to verify that table metadata is not negatively cached and that null is correctly returned when a table is not found, ensuring the fix works as expected.

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[gemini-code-assist]

Code Review

This pull request successfully disables negative caching for table metadata by migrating from Guava Cache to Caffeine. The changes are logical and correctly leverage Caffeine's behavior of not caching null values. The accompanying tests are thorough and validate the new caching strategy, especially the absence of negative caching.

I've added a couple of suggestions to use modern Java features like method references and lambda expressions to replace anonymous inner classes. These changes will make the code more concise and readable.

[Copilot]

Copilot encountered an error and was unable to review this pull request. You can try again by re-requesting a review.

[Torch3333]

LGTM, thank you!

[jnmt]

LGTM, thank you!

[komamitsu]

LGTM! 👍

[feeblefakie]

LGTM! Thank you!